AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
提供方
AI for Medicine 专项课程deeplearning.ai课程信息
学生职业成果
50%
完成这些课程后已开始新的职业生涯
25%
通过此课程获得实实在在的工作福利
33%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 1 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
完成时间大约为30 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将学到的内容有
Walk through examples of prognostic tasks
Apply tree-based models to estimate patient survival rates
Navigate practical challenges in medicine like missing data
您将获得的技能
Deep LearningMachine Learningtime-to-event modelingRandom Forestmodel tuning
学生职业成果
50%
完成这些课程后已开始新的职业生涯
25%
通过此课程获得实实在在的工作福利
33%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 1 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
完成时间大约为30 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
教学大纲 - 您将从这门课程中学到什么
完成时间为 9 小时
Linear prognostic models
Build a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index. Finally, improve the model by adding feature interactions.
完成时间为 9 小时
11 个视频 （总计 28 分钟）, 4 个阅读材料, 2 个测验
完成时间为 7 小时
Prognosis with Tree-based models
Tune decision tree and random forest models to predict the risk of a disease. Evaluate the model performance using the c-index. Identify missing data and how it may alter the data distribution, then use imputation to fill in missing data, in order to improve model performance.
完成时间为 7 小时
15 个视频 （总计 41 分钟）
完成时间为 6 小时
Survival Models and Time
This week, you will work with data where the time that a disease occurs is a variable. Instead of predicting just the 10-year risk of a disease, you will build more flexible models that can predict the 5 year, 7 year, or 10 year risk.
完成时间为 6 小时
16 个视频 （总计 38 分钟）
完成时间为 8 小时
Build a risk model using linear and tree-based models
This week, you will fit a linear model, and a tree-based risk model on survival data, to customize a risk score for each patient, based on their health profile. The risk score represents the patient’s relative risk of getting a particular disease. You will then evaluate each model’s performance by implementing and using a concordance index that incorporates time to event and censored data.
完成时间为 8 小时
24 个视频 （总计 69 分钟）, 3 个阅读材料, 2 个测验
审阅
来自AI FOR MEDICAL PROGNOSIS 的热门评论
由 JD 提供Jun 4, 2020
I am a medical image analysis enthusiast. But I always wonder why I can't I combine other patient details for extending it's application. Sure this course is awesome. I really loved it !!
由 BM 提供Apr 21, 2020
This course was great and more challenging that I have expected. More focus on statistics and survival data which is important for prognosis. Course has a good flow and valuable content.
由 RR 提供Sep 8, 2020
This course is one of the best courses to learn about Medical Prognosis. Really, the survival models were described in great detail. Thank you, Pranav for this wonderful course.
由 PP 提供Jan 20, 2021
Def an eye opening course with all the various NLP libraries and also the survival modeling. The content was clear and easy to understand if you have the required background.
关于 AI for Medicine 专项课程
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
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